R/convGDX2MIF.R
In pik-piam/limes: The liMES R package
Defines functions
convGDX2MIF
Documented in
convGDX2MIF
#' Read in GDX and write *.mif reporting
#'
#' Read in all information from GDX file and create
#' the *.mif reporting
#'
#'
#' @param gdx a GDX as created by readGDX, or the file name of a gdx
#' @param gdx_ref reference-gdx for policy costs, a GDX as created by readGDX, or the file name of a gdx
#' @param file name of the mif file which will be written, if no name is
#' provided a magpie object containing all the reporting information is
#' returned
#' @param scenario scenario name that is used in the *.mif reporting
#' @param time temporal resolution of the reporting, default: c(seq(2010,2050,5))
#' @author Sebastian Osorio and Renato Rodrigues
#' @examples
#'
#' \dontrun{convGDX2MIF(gdx,gdx_ref,file="LIMES_generic_default.csv",scenario="default")}
#'
#' @export
#' @importFrom magclass mbind write.report getNames getItems<- getItems
convGDX2MIF <- function(gdx,gdx_ref=NULL,file=NULL,scenario="default", time=as.numeric(readGDX(gdx,name="t"))) {
#time=as.numeric(readGDX(gdx,name="t"))
# generate the report
#initialize report variable
output <- NULL
#adding capacity info to report output
output <- mbind(output,reportCapacity(gdx)[,c(time),])
#output <- mbind(output,limes:::reportCapacity(gdx)[,time,]) # run this line when doign just some test
#adding primary energy to report output
output <- mbind(output,reportPrimaryEnergy(gdx)[,time,])
#adding fuel costs to report output
output <- mbind(output,reportFuelCosts(gdx)[,time,])
#adding electricity generation info to report output
output <- mbind(output,reportGeneration(gdx,output)[,time,]) #dependent on primary energy and fuel costs
#adding electricity prices info to report output
output <- mbind(output,reportElectricityPrices(gdx)[,time,])
#adding demand info to report output
output <- mbind(output,reportDemand(gdx,output)[,time,]) #dependent on generation and primary energy
#adding availability factors to report output
output <- mbind(output,reportLoadFactor(gdx,output)[,time,]) #Depends on Generation and Capacity
#Adding all the input parameters (except for fuel costs)
#output <- mbind(output,reportInput(gdx)[,time,])
#adding CO2 price to report output
output <- mbind(output,reportCO2Price(gdx)[,time,])
#adding emissions info to report output
output <- mbind(output,reportEmissions(gdx,output)[,time,]) #depending on generation
#adding industry emissions to report output
output <- mbind(output,reportIndustryEmissions(gdx,output)[,time,]) #depending on CO2 price and emissions
#adding capital costs to report output
output <- mbind(output,reportCapitalCosts(gdx)[,time,])
#adding buildings to report output
output <- mbind(output,reportBuildings(gdx,output)[,time,]) #Depends on reportGeneration
#adding peak demand to report output (now included in reportDemand)
#output <- mbind(output,reportPeakDemand(gdx)[,time,])
#adding curtailment to report output
#output <- mbind(output,reportCurtailment(gdx)[,time,]) #now on generation
#adding carbon sequestration to report output
#output <- mbind(output,reportCarbonSequestration(gdx)[,time,]) #Now on emissions
#adding capacity additions to report output
output <- mbind(output,reportCapacityAdditions(gdx)[,time,])
#adding capacity disinvestments to report output
#output <- mbind(output,reportDisinvestments(gdx)[,time,])
#adding exchange to report output
output <- mbind(output,reportExchange(gdx)[,time,])
#adding carbon sequestration to report output
output <- mbind(output,reportTotalSystemCosts(gdx,output)[,time,]) #depends on reportExchange and reportCO2Price
#adding adequacy contribution to report output
output <- mbind(output,reportAdequacyContribution(gdx)[,time,])
#adding fictitious vars to report output. These variables are later erased and only the aggregated (updated) values are left
#(this is needed to keep report within the dimensions)
#An example is the cap for the EU ETS
output <- mbind(output,reportFictitiousVars(gdx,output)[,time,])
#Replace NAs by zeros to avoid missing variables when aggregating variables
output[is.na(output)] <- 0
#Save file before aggregation
output_beforeagg <- output
#output <- output_beforeagg
#AGGREGATE (WEIGHTED AVERAGE OF) SOME INTENSIVE VARIABLES (e.g., electricity price)
output_RegAgg <- limesInt2Ext(gdx,output)
#LOADING LIST OF REGIONS FOR AGGREGATING CERTAIN GROUPS (e.g., EU)
# settings mapping path
mappingregiPath <- system.file("extdata","LIMES_country_ISO_3.csv",package="limes")
# reading mapping file
mappingregi <- read.csv(mappingregiPath,sep=";")
#aggregating all countries
output_tot <- dimSums(output,dim=1, na.rm = T)
getItems(output_tot, dim = 1) <- "GLO"
#Replacing the aggregated for intensive variables (a sum that makes no sense) by the weighted average calculated above
output_tot[,,getNames(output_RegAgg)] <- output_RegAgg["GLO",,]
#aggregating only EU-28
EU<-which(getItems(output, dim = 1) != "NOR" & getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "GLO")
output_EU<-NULL
output_EU<-dimSums(output[EU,,],dim=1, na.rm = T)
getItems(output_EU, dim = 1) <- "EU28"
output_EU[,,getNames(output_RegAgg)] <- output_RegAgg["EU28",,]
#aggregating only EU-27
EU27<-which(getItems(output, dim = 1) != "GBR" & getItems(output, dim = 1) != "NOR" & getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "GLO")
output_EU27<-NULL
output_EU27<-dimSums(output[EU27,,],dim=1, na.rm = T)
getItems(output_EU27, dim = 1) <- "EU27"
output_EU27[,,getNames(output_RegAgg)] <- output_RegAgg["EU27",,]
#aggregating only EU-ETS
output_EUETS<-NULL
#The definition of EU ETS in LIMES depend on the implementation of the linking switch between EU ETS and UK ETS
#Before that, the banking constraint and all MSR-related variables, i.e., emissions and allowances,
#accounted for UK within the EU ETS
c_linkEUETS_UK <- readGDX(gdx, name = c("c_linkEUETS_UK"), field = "l", format = "first_found", react = 'silent') #link between EU ETS and UK ETS, and thus when B
if(is.null(c_linkEUETS_UK)) {
EUETS<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "GLO")
output_EUETS<-dimSums(output[EUETS,,],dim=1, na.rm = T)
} else {
EUETS_pre2020<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "GLO")
EUETS_post2020<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "GLO" & getItems(output, dim = 1) != "GBR")
#ETS until 2020 contains UK
output_EUETS<-dimSums(output[EUETS_pre2020,,],dim=1, na.rm = T)
#ETS after 2020 does not contain UK
output_EUETS[,setdiff(time,c(2010:2020)),]<-dimSums(output[EUETS_post2020,setdiff(time,c(2010:2020)),],dim=1, na.rm = T)
}
getItems(output_EUETS, dim = 1)<-"EUETS"
output_EUETS[,,getNames(output_RegAgg)] <- output_RegAgg["EUETS",,] #Add intensive variables
#aggregating EUETS-nonDE
output_EUETS_nonDE<-NULL
if(is.null(c_linkEUETS_UK)) {
EUETS_nonDE<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "DEU" & getItems(output, dim = 1) != "GLO")
output_EUETS_nonDE<-dimSums(output[EUETS_nonDE,,],dim=1, na.rm = T)
} else {
EUETS_nonDE_pre2020<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "DEU" & getItems(output, dim = 1) != "GLO")
EUETS_nonDE_post2020<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "DEU" & getItems(output, dim = 1) != "GLO" & getItems(output, dim = 1) != "GBR")
#ETS until 2020 contains UK
output_EUETS_nonDE<-dimSums(output[EUETS_nonDE_pre2020,,],dim=1, na.rm = T)
#ETS after 2020 does not contain UK
output_EUETS_nonDE[,setdiff(time,c(2010:2020)),]<-dimSums(output[EUETS_nonDE_post2020,setdiff(time,c(2010:2020)),],dim=1, na.rm = T)
}
getItems(output_EUETS_nonDE, dim = 1)<-"EUETS_nonDE"
#Showing KdW results is rarely necessary. Apply a switch (0) No (1) Yes
show_KdW <- 0
if (show_KdW == 1) {
#aggregating KdW (coalition of the willing) and nonKdW
KdW_iso2 <- readGDX(gdx,name="regi_KdW")
KdW_iso3 <- mappingregi[match(KdW_iso2,mappingregi[,1]),2]
#KdW <- which(mappingregi$KdW == 1)
KdW <- KdW_iso3 #Better to take it directly from the GDX file
output_KdW<-dimSums(output[KdW,,],dim=1, na.rm = T)
getItems(output_KdW, dim = 1)<-"KdW"
nonKdW <- which(mappingregi$KdW == 0)
output_nonKdW<-dimSums(output[nonKdW,,],dim=1, na.rm = T)
getItems(output_nonKdW, dim = 1)<-"nonKdW"
#aggregating KdW_EU, KdW_nonEU, nonKdW_EU, nonKdW_nonEU
KdW_EU <- which(mappingregi$KdW == 1 & mappingregi$EU == 1)
output_KdW_EU<-dimSums(output[KdW_EU,,],dim=1, na.rm = T)
getItems(output_KdW_EU, dim = 1)<-"KdW_EU"
KdW_nonEU <- which(mappingregi$KdW == 1 & mappingregi$EU == 0)
output_KdW_nonEU<-dimSums(output[KdW_nonEU,,],dim=1, na.rm = T)
getItems(output_KdW_nonEU, dim = 1)<-"KdW_nonEU"
nonKdW_EU <- which(mappingregi$KdW == 0 & mappingregi$EU == 1)
output_nonKdW_EU<-dimSums(output[nonKdW_EU,,],dim=1, na.rm = T)
getItems(output_nonKdW_EU, dim = 1)<-"nonKdW_EU"
nonKdW_nonEU <- which(mappingregi$KdW == 0 & mappingregi$EU == 0)
output_nonKdW_nonEU<-dimSums(output[nonKdW_nonEU,,],dim=1, na.rm = T)
getItems(output_nonKdW_nonEU, dim = 1)<-"nonKdW_nonEU"
#totals concerning the KdW
output <- mbind(output,output_KdW,output_nonKdW,output_KdW_EU,output_KdW_nonEU,output_nonKdW_EU,output_nonKdW_nonEU)
}
#load regions that implemented a top-up minimum CO2 price
minP <- readGDX(gdx,name="regi_minP")
#output_aggminP <- NULL
#convert these regions to iso3
if(length(minP) > 0) {
minP_iso3 <- mappingregi[match(minP,mappingregi[,1]),2]
output_minP<-dimSums(output[minP_iso3,,],dim=1, na.rm = T)
getItems(output_minP, dim = 1)<-"minP"
output_nonminP<-output_tot - output_minP
getItems(output_nonminP, dim = 1)<-"non_minP"
output_EUnonminP<-dimSums(output[EU,,],dim=1, na.rm = T) - output_minP
getItems(output_EUnonminP, dim = 1)<-"EU_non_minP"
output <- mbind(output,output_minP,output_nonminP,output_EUnonminP)
}
#CONCATENATING OUTPUT FROM REGIONS AND AGGREGATED DATA
output <- mbind(output,output_tot,output_EU,output_EU27,output_EUETS,output_EUETS_nonDE)
#totals concerning countries implementing min CO2 price is done above
#totals concerning countries KdW is done above
#INCLUDING ONLY CERTAIN VARIABLES
# settings mapping path
mappingvarsPath <- system.file("extdata","MappingVars.csv",package="limes")
# reading mapping file
mappingvars <- read.csv(mappingvarsPath,sep=";", dec = ".")
# write the *.mif or give back the magpie opject output
finalvars <- paste0(as.vector(mappingvars$LIMES)," (",as.vector(mappingvars$UnitLIMES) , ")")
output <- output[,,intersect(finalvars,getNames(output))]
#ERASING INTENSIVE VARIABLES (World, EU28, etc) for variables that were not weighted
# settings mapping path
mappingPath <- system.file("extdata","WeightedAverageVars.csv",package="limes")
# reading mapping file
mapping_int2ext <- read.csv(mappingPath,sep=";")
# Write the 2 sets: all intensive variables and those that were not averaged
#a) All
pos_intall <- match(mapping_int2ext$int, mappingvars$LIMES)
pos_intall <- pos_intall[!is.na(pos_intall)]
IntVars <- paste0(mappingvars$LIMES[pos_intall]," (",mappingvars$UnitLIMES[pos_intall],")")
#IntVars <- paste0(as.vector(mappingvars$LIMES[pos_intall])," (",as.vector(mappingvars$UnitLIMES[pos_intall]) , ")")
#Make sure this variable was indeed calculated
IntVars <- intersect(finalvars,IntVars)
#b) non-weighted intensive variables
mapping_int2ext_noweight <- mapping_int2ext[mapping_int2ext$ext == 0,]
pos_intnoweight <- match(mapping_int2ext_noweight$int, mappingvars$LIMES)
pos_intnoweight <- pos_intnoweight[!is.na(pos_intnoweight)]
IntVars_noweight <- paste0(mappingvars$LIMES[pos_intnoweight]," (",mappingvars$UnitLIMES[pos_intnoweight],")")
#Make sure this variable was indeed calculated
IntVars_noweight <- intersect(finalvars,IntVars_noweight)
IntVars_noweight <- setdiff(IntVars_noweight,c("Capacity|Electricity|Transmission Grid (GW)","Capacity|Electricity|Transmission Grid-km (GWkm)"))
if (length(intersect(getNames(output),IntVars)) > 0) {
output["GLO",,intersect(getNames(output),IntVars_noweight)] <- NA
output["EU28",,intersect(getNames(output),IntVars_noweight)] <- NA
output["EU27",,intersect(getNames(output),IntVars_noweight)] <- NA
output["EUETS",,intersect(getNames(output),IntVars_noweight)] <- NA
output["EUETS_nonDE",,intersect(getNames(output),IntVars)] <- NA
if (show_KdW == 1) {
output["KdW",,intersect(getNames(output),IntVars)] <- NA
output["nonKdW",,intersect(getNames(output),IntVars)] <- NA
output["KdW_EU",,intersect(getNames(output),IntVars)] <- NA
output["KdW_nonEU",,intersect(getNames(output),IntVars)] <- NA
output["nonKdW_EU",,intersect(getNames(output),IntVars)] <- NA
output["nonKdW_nonEU",,intersect(getNames(output),IntVars)] <- NA
}
if(length(minP) > 0) {
output["minP",,intersect(getNames(output),IntVars)] <- NA
output["non_minP",,intersect(getNames(output),IntVars)] <- NA
output["EU_non_minP",,intersect(getNames(output),IntVars)] <- NA
output["EUETS_non_minP",,intersect(getNames(output),IntVars)] <- NA
}
}
#Transmission capacity aggregated (special case)
if(length(intersect(getNames(output),"Capacity|Electricity|Transmission Grid (GW)")) > 0) {
output[c("GLO","EU28","EU27","EUETS"),,"Capacity|Electricity|Transmission Grid (GW)"] <-
output[c("GLO","EU28","EU27","EUETS"),,"Capacity|Electricity|Transmission Grid (GW)"]/2
output[c("GLO","EU28","EU27","EUETS"),,"Capacity|Electricity|Transmission Grid-km (GWkm)"] <-
output[c("GLO","EU28","EU27","EUETS"),,"Capacity|Electricity|Transmission Grid-km (GWkm)"]/2
}
#ADDING VARIABLES THAT ONLY EXIST FOR AN AGGREGATED REGION, e.g., the EU ETS cap
#(not possible to do as default for the region-dependent variables because mbind only works when dimensions do not differ)
# settings mapping path
AggVarPath <- system.file("extdata","AggregateVariables.csv",package="limes")
# reading mapping file
AggVarfile <- read.csv(AggVarPath,sep=";")
# write the *.mif or give back the magpie opject output
AggVars_tmp <- paste0(as.vector(AggVarfile$LIMES)," (",as.vector(AggVarfile$UnitLIMES) , ")")
AggVars <- intersect(AggVars_tmp,getNames(output))
#Depending on the configuration, some variables might have national values and thus should be extracted from the variables to be calculated for EU ETS
#and incorporated in the report as follows
#This variables appear already in output (from calculations or as fictitious variables), so we need to identify them (values differ from 0)
#Those which sum of values for region GLO (total) are 0, are those that have not been calculated
AggVars <- AggVars[which(dimSums(output["GLO",,AggVars], dim = 2) == 0)]
#Adding the corresponding values for the EU ETS
output_EUETSvars <- reportEUETSvars(gdx,output)[,time,] #dependent on reportEmissions
output["EUETS",,intersect(AggVars,getNames(output_EUETSvars))] <- output_EUETSvars[,,intersect(AggVars,getNames(output_EUETSvars))]
output_MSR <- reportMSR(gdx)[,time,]
output["EUETS",,intersect(AggVars,getNames(output_MSR))] <- output_MSR[,,intersect(AggVars,getNames(output_MSR))]
#Erase the values from the other fictitious variables for which there are no real values
output["EUETS",,setdiff(AggVars,union(getNames(output_EUETSvars),getNames(output_MSR)))] <- NA
#output["EUETS",,AggVars] <- reportEUETSvars(gdx)[,time,AggVars]
#Erasing the values for the remaining regions
output[setdiff(getItems(output, dim = 1),"EUETS"),,AggVars] <- NA
##UK ETS
#Adding the corresponding values for the EU ETS
output_UKETSvars <- reportUKETSvars(gdx,output)[,time,] #dependent on reportEUETSvars
output["GBR",,intersect(AggVars,getNames(output_UKETSvars))] <- output_UKETSvars[,,intersect(AggVars,getNames(output_UKETSvars))]
#INCLUDE HISTORICAL VALUES FOR THE INDUSTRY
#(source: "REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT AND THE COUNCIL Report on the functioning of the European carbon market COM/2019/557 final/2" https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52019DC0557R(01))
if(!is.na(match("Emissions|CO2|Industry (Mt CO2/yr)",getNames(output)))) {
output["EUETS",2010,"Emissions|CO2|Industry (Mt CO2/yr)"] <- NA #715 #Value from 2011 (2010 not available) - emissions reported from data viewer (537.4) might be incomplete as some could be included in combustion
#output["EUETS",2015,"Emissions|CO2|Industry (Mt CO2/yr)"] <- 771 #emissions reported from data viewer (590.8) might be incomplete as some could be included in combustion
output["EUETS",2010,"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"] <- NA #output["EUETS",2010,"Emissions|CO2|Industry (Mt CO2/yr)"] + output["EUETS",2010,"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"]
#output["EUETS",2015,"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"] <- output["EUETS",2015,"Emissions|CO2|Industry (Mt CO2/yr)"] + output["EUETS",2015,"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"]
if(!is.na(match("Emissions|CO2|EU ETS (Mt CO2/yr)",getNames(output)))) {
output["EUETS",2010,"Emissions|CO2|EU ETS (Mt CO2/yr)"] <- NA #output["EUETS",2010,"Emissions|CO2|Industry (Mt CO2/yr)"] + output["EUETS",2010,"Emissions|CO2|EU ETS (Mt CO2/yr)"]
#output["EUETS",2015,"Emissions|CO2|EU ETS (Mt CO2/yr)"] <- output["EUETS",2015,"Emissions|CO2|Industry (Mt CO2/yr)"] + output["EUETS",2015,"Emissions|CO2|EU ETS (Mt CO2/yr)"]
}
#To avoid confusion, make sure that industry-related values are not reported for the EU28
output["EU28",c(2010,2015),"Emissions|CO2|Industry (Mt CO2/yr)"] <- NA
output["EU27",c(2010,2015),"Emissions|CO2|Industry (Mt CO2/yr)"] <- NA
output["EU28",c(2010,2015),"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"] <- NA
output["EU27",c(2010,2015),"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"] <- NA
#Add NA for 2010. Because the MAC industry only applies from 2015, the reported price is the sum
if(is.na(output[c("GLO"),c(2010),"Emissions|CO2|Industry (Mt CO2/yr)"])) {
output[c("EU28","EUETS","EU27","GLO"),c(2010),"Price|Carbon|Net|Industry (Eur2010/t CO2)"] <- NA
output[c("EU28","EUETS","EU27","GLO"),c(2010),"Price|Carbon|National Climate Target|Industry (Eur2010/t CO2)"] <- NA
}
}
#SCALING THE RESULTS ACCORDING TO THE UNITS SPECIFIES FOR THE PROJECT
tmp <- NULL
for (i in seq_len(length(getNames(output)))) {
tmp <- mbind(tmp,output[,,i]*mappingvars[match(getNames(output)[i],finalvars),]$ConvFactor)
}
output_f <- tmp
#output<-tmp
#output_f<-output
#MAPPING THE VARIABLES TO THOSE OF A SPECIFIC PROJECT
posvarsmapping <- match(getNames(output_f),finalvars)
posvarsmapping <- posvarsmapping[which(posvarsmapping != "NA")]
mappingvars_project <- paste0(as.vector(mappingvars[posvarsmapping,]$Enavi)," (",as.vector(mappingvars[posvarsmapping,]$UnitEnavi) , ")")
output_f <- setNames(output_f,mappingvars_project)
#REPORT FIGURES
#reportFigures(gdx,output_f)
#WRITE REPORT
#load the model version
c_LIMESversion <- readGDX(gdx,name="c_LIMESversion",field="l",format="first_found") #model version
if(!is.null(file)) {
write.report(output_f,model=paste0("LIMES_EU_v",c_LIMESversion),scenario=scenario,file=file,ndigit=7)
}
#if(!is.null(file)) write.reportProject(paste0("LIMES_generic_",scenario,".mif"),mappingvars,model="LIMES_EU",scenario=scenario,file=file,ndigit=7)
invisible(output_f)
#################################################################
#ONLY FOR THE REPORT OF INPUTS (run manually)
#################################################################
#Always keep this in 0!!!!
#c_reportinput <- 0
#
#if(c_reportinput != 0) {
# #time=as.numeric(readGDX(gdx,name="t"))
#
# #Files-related variables
# #outputdir <- "C:/Users/osorio/Nextcloud/PIK/Data for LIMES/CSV files/output" # path to the output folder
# limes_reporting_file <- file.path(outputdir,paste0("LIMES_inputparam.mif"))
# file=limes_reporting_file
# #lastDir <- splitPath[[1]][length(splitPath[[1]])]
# #scenario <- lastDir
#
# #initialize report variable
# output <- NULL
#
# #adding fuel costs to report output
# output <- mbind(output,reportFuelCosts(gdx)[,time,])
#
# #Adding all the input parameters (except for fuel costs) -> turn on switch (c_reportheating) if heat-related input is to be reported
# output <- mbind(output,reportInput(gdx)[,time,])
#
# #Clean the output
# output_glo <- NULL
# var_dup <- NULL
# n_regi <- length(unique(getItems(output, dim = 1)))
# n_years <- length(getYears(output))
# for (var_name in getNames(output)) {
# #Create array to save whether the number is duplicated for all REGI in one YEAR
# dup_year <- new.magpie(cells_and_regions = "GLO", years = getYears(output), names = NULL,
# fill = 0, sort = FALSE, sets = NULL)
# #Create array to save duplicated values in just one ("GLO") array
# #output_tmp <- new.magpie(cells_and_regions = "GLO", years = getYears(output), names = NULL,
# # fill = 0, sort = FALSE, sets = NULL)
#
# for (tt in getYears(output)) {
# #Check if the value is duplicated for all REGI in one YEAR
# if(sum(duplicated(output[,tt,var_name])) == n_regi-1) {
# dup_year[,tt,] <- 1
# }
# }
#
# #Check if duplicates occur over all YEARS
# #output_tmp <- NULL
# if(dimSums(dup_year[,,],dim=2)==n_years) {
# #Allocate DEU values to the unique "GLO" variable
# output_tmp <- output["DEU",,var_name]
# var_dup <- c(var_dup,var_name)
# } else {
# output_tmp <- 0*output["DEU",,var_name]
# }
# getItems(output_tmp, dim = 1) <- "GLO"
#
# #Concatenate all "GLO" variables
# output_glo <- mbind(output_glo,output_tmp)
# }
#
# #Concatenate "GLO" variables with (surviving) region-dependent parameters
# output_f <- mbind(output_glo,output)
#
# #Clean the file
# #Erase region-dependent data for variables with duplicates
# output_f[setdiff(getItems(output_f, dim = 1),"GLO"),,var_dup] <- NA
# #Erase "GLO" data for variables without duplicates
# output_f["GLO",,setdiff(getNames(output_f),var_dup)] <- NA
#
# c_LIMESversion <- readGDX(gdx,name="c_LIMESversion",field="l",format="first_found") #model version
# if(!is.null(file)) write.report(output_f,model=paste0("LIMES_EU_v",c_LIMESversion),scenario=scenario,file=file,ndigit=7)
#}
}
pik-piam/limes documentation built on May 1, 2024, 6:17 a.m.
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Defines functions convGDX2MIF
Documented in convGDX2MIF
#' Read in GDX and write *.mif reporting
#'
#' Read in all information from GDX file and create
#' the *.mif reporting
#'
#'
#' @param gdx a GDX as created by readGDX, or the file name of a gdx
#' @param gdx_ref reference-gdx for policy costs, a GDX as created by readGDX, or the file name of a gdx
#' @param file name of the mif file which will be written, if no name is
#' provided a magpie object containing all the reporting information is
#' returned
#' @param scenario scenario name that is used in the *.mif reporting
#' @param time temporal resolution of the reporting, default: c(seq(2010,2050,5))
#' @author Sebastian Osorio and Renato Rodrigues
#' @examples
#'
#' \dontrun{convGDX2MIF(gdx,gdx_ref,file="LIMES_generic_default.csv",scenario="default")}
#'
#' @export
#' @importFrom magclass mbind write.report getNames getItems<- getItems
convGDX2MIF <- function(gdx,gdx_ref=NULL,file=NULL,scenario="default", time=as.numeric(readGDX(gdx,name="t"))) {
#time=as.numeric(readGDX(gdx,name="t"))
# generate the report
#initialize report variable
output <- NULL
#adding capacity info to report output
output <- mbind(output,reportCapacity(gdx)[,c(time),])
#output <- mbind(output,limes:::reportCapacity(gdx)[,time,]) # run this line when doign just some test
#adding primary energy to report output
output <- mbind(output,reportPrimaryEnergy(gdx)[,time,])
#adding fuel costs to report output
output <- mbind(output,reportFuelCosts(gdx)[,time,])
#adding electricity generation info to report output
output <- mbind(output,reportGeneration(gdx,output)[,time,]) #dependent on primary energy and fuel costs
#adding electricity prices info to report output
output <- mbind(output,reportElectricityPrices(gdx)[,time,])
#adding demand info to report output
output <- mbind(output,reportDemand(gdx,output)[,time,]) #dependent on generation and primary energy
#adding availability factors to report output
output <- mbind(output,reportLoadFactor(gdx,output)[,time,]) #Depends on Generation and Capacity
#Adding all the input parameters (except for fuel costs)
#output <- mbind(output,reportInput(gdx)[,time,])
#adding CO2 price to report output
output <- mbind(output,reportCO2Price(gdx)[,time,])
#adding emissions info to report output
output <- mbind(output,reportEmissions(gdx,output)[,time,]) #depending on generation
#adding industry emissions to report output
output <- mbind(output,reportIndustryEmissions(gdx,output)[,time,]) #depending on CO2 price and emissions
#adding capital costs to report output
output <- mbind(output,reportCapitalCosts(gdx)[,time,])
#adding buildings to report output
output <- mbind(output,reportBuildings(gdx,output)[,time,]) #Depends on reportGeneration
#adding peak demand to report output (now included in reportDemand)
#output <- mbind(output,reportPeakDemand(gdx)[,time,])
#adding curtailment to report output
#output <- mbind(output,reportCurtailment(gdx)[,time,]) #now on generation
#adding carbon sequestration to report output
#output <- mbind(output,reportCarbonSequestration(gdx)[,time,]) #Now on emissions
#adding capacity additions to report output
output <- mbind(output,reportCapacityAdditions(gdx)[,time,])
#adding capacity disinvestments to report output
#output <- mbind(output,reportDisinvestments(gdx)[,time,])
#adding exchange to report output
output <- mbind(output,reportExchange(gdx)[,time,])
#adding carbon sequestration to report output
output <- mbind(output,reportTotalSystemCosts(gdx,output)[,time,]) #depends on reportExchange and reportCO2Price
#adding adequacy contribution to report output
output <- mbind(output,reportAdequacyContribution(gdx)[,time,])
#adding fictitious vars to report output. These variables are later erased and only the aggregated (updated) values are left
#(this is needed to keep report within the dimensions)
#An example is the cap for the EU ETS
output <- mbind(output,reportFictitiousVars(gdx,output)[,time,])
#Replace NAs by zeros to avoid missing variables when aggregating variables
output[is.na(output)] <- 0
#Save file before aggregation
output_beforeagg <- output
#output <- output_beforeagg
#AGGREGATE (WEIGHTED AVERAGE OF) SOME INTENSIVE VARIABLES (e.g., electricity price)
output_RegAgg <- limesInt2Ext(gdx,output)
#LOADING LIST OF REGIONS FOR AGGREGATING CERTAIN GROUPS (e.g., EU)
# settings mapping path
mappingregiPath <- system.file("extdata","LIMES_country_ISO_3.csv",package="limes")
# reading mapping file
mappingregi <- read.csv(mappingregiPath,sep=";")
#aggregating all countries
output_tot <- dimSums(output,dim=1, na.rm = T)
getItems(output_tot, dim = 1) <- "GLO"
#Replacing the aggregated for intensive variables (a sum that makes no sense) by the weighted average calculated above
output_tot[,,getNames(output_RegAgg)] <- output_RegAgg["GLO",,]
#aggregating only EU-28
EU<-which(getItems(output, dim = 1) != "NOR" & getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "GLO")
output_EU<-NULL
output_EU<-dimSums(output[EU,,],dim=1, na.rm = T)
getItems(output_EU, dim = 1) <- "EU28"
output_EU[,,getNames(output_RegAgg)] <- output_RegAgg["EU28",,]
#aggregating only EU-27
EU27<-which(getItems(output, dim = 1) != "GBR" & getItems(output, dim = 1) != "NOR" & getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "GLO")
output_EU27<-NULL
output_EU27<-dimSums(output[EU27,,],dim=1, na.rm = T)
getItems(output_EU27, dim = 1) <- "EU27"
output_EU27[,,getNames(output_RegAgg)] <- output_RegAgg["EU27",,]
#aggregating only EU-ETS
output_EUETS<-NULL
#The definition of EU ETS in LIMES depend on the implementation of the linking switch between EU ETS and UK ETS
#Before that, the banking constraint and all MSR-related variables, i.e., emissions and allowances,
#accounted for UK within the EU ETS
c_linkEUETS_UK <- readGDX(gdx, name = c("c_linkEUETS_UK"), field = "l", format = "first_found", react = 'silent') #link between EU ETS and UK ETS, and thus when B
if(is.null(c_linkEUETS_UK)) {
EUETS<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "GLO")
output_EUETS<-dimSums(output[EUETS,,],dim=1, na.rm = T)
} else {
EUETS_pre2020<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "GLO")
EUETS_post2020<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "GLO" & getItems(output, dim = 1) != "GBR")
#ETS until 2020 contains UK
output_EUETS<-dimSums(output[EUETS_pre2020,,],dim=1, na.rm = T)
#ETS after 2020 does not contain UK
output_EUETS[,setdiff(time,c(2010:2020)),]<-dimSums(output[EUETS_post2020,setdiff(time,c(2010:2020)),],dim=1, na.rm = T)
}
getItems(output_EUETS, dim = 1)<-"EUETS"
output_EUETS[,,getNames(output_RegAgg)] <- output_RegAgg["EUETS",,] #Add intensive variables
#aggregating EUETS-nonDE
output_EUETS_nonDE<-NULL
if(is.null(c_linkEUETS_UK)) {
EUETS_nonDE<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "DEU" & getItems(output, dim = 1) != "GLO")
output_EUETS_nonDE<-dimSums(output[EUETS_nonDE,,],dim=1, na.rm = T)
} else {
EUETS_nonDE_pre2020<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "DEU" & getItems(output, dim = 1) != "GLO")
EUETS_nonDE_post2020<-which(getItems(output, dim = 1) != "CHE" & getItems(output, dim = 1) != "BAL" & getItems(output, dim = 1) != "DEU" & getItems(output, dim = 1) != "GLO" & getItems(output, dim = 1) != "GBR")
#ETS until 2020 contains UK
output_EUETS_nonDE<-dimSums(output[EUETS_nonDE_pre2020,,],dim=1, na.rm = T)
#ETS after 2020 does not contain UK
output_EUETS_nonDE[,setdiff(time,c(2010:2020)),]<-dimSums(output[EUETS_nonDE_post2020,setdiff(time,c(2010:2020)),],dim=1, na.rm = T)
}
getItems(output_EUETS_nonDE, dim = 1)<-"EUETS_nonDE"
#Showing KdW results is rarely necessary. Apply a switch (0) No (1) Yes
show_KdW <- 0
if (show_KdW == 1) {
#aggregating KdW (coalition of the willing) and nonKdW
KdW_iso2 <- readGDX(gdx,name="regi_KdW")
KdW_iso3 <- mappingregi[match(KdW_iso2,mappingregi[,1]),2]
#KdW <- which(mappingregi$KdW == 1)
KdW <- KdW_iso3 #Better to take it directly from the GDX file
output_KdW<-dimSums(output[KdW,,],dim=1, na.rm = T)
getItems(output_KdW, dim = 1)<-"KdW"
nonKdW <- which(mappingregi$KdW == 0)
output_nonKdW<-dimSums(output[nonKdW,,],dim=1, na.rm = T)
getItems(output_nonKdW, dim = 1)<-"nonKdW"
#aggregating KdW_EU, KdW_nonEU, nonKdW_EU, nonKdW_nonEU
KdW_EU <- which(mappingregi$KdW == 1 & mappingregi$EU == 1)
output_KdW_EU<-dimSums(output[KdW_EU,,],dim=1, na.rm = T)
getItems(output_KdW_EU, dim = 1)<-"KdW_EU"
KdW_nonEU <- which(mappingregi$KdW == 1 & mappingregi$EU == 0)
output_KdW_nonEU<-dimSums(output[KdW_nonEU,,],dim=1, na.rm = T)
getItems(output_KdW_nonEU, dim = 1)<-"KdW_nonEU"
nonKdW_EU <- which(mappingregi$KdW == 0 & mappingregi$EU == 1)
output_nonKdW_EU<-dimSums(output[nonKdW_EU,,],dim=1, na.rm = T)
getItems(output_nonKdW_EU, dim = 1)<-"nonKdW_EU"
nonKdW_nonEU <- which(mappingregi$KdW == 0 & mappingregi$EU == 0)
output_nonKdW_nonEU<-dimSums(output[nonKdW_nonEU,,],dim=1, na.rm = T)
getItems(output_nonKdW_nonEU, dim = 1)<-"nonKdW_nonEU"
#totals concerning the KdW
output <- mbind(output,output_KdW,output_nonKdW,output_KdW_EU,output_KdW_nonEU,output_nonKdW_EU,output_nonKdW_nonEU)
}
#load regions that implemented a top-up minimum CO2 price
minP <- readGDX(gdx,name="regi_minP")
#output_aggminP <- NULL
#convert these regions to iso3
if(length(minP) > 0) {
minP_iso3 <- mappingregi[match(minP,mappingregi[,1]),2]
output_minP<-dimSums(output[minP_iso3,,],dim=1, na.rm = T)
getItems(output_minP, dim = 1)<-"minP"
output_nonminP<-output_tot - output_minP
getItems(output_nonminP, dim = 1)<-"non_minP"
output_EUnonminP<-dimSums(output[EU,,],dim=1, na.rm = T) - output_minP
getItems(output_EUnonminP, dim = 1)<-"EU_non_minP"
output <- mbind(output,output_minP,output_nonminP,output_EUnonminP)
}
#CONCATENATING OUTPUT FROM REGIONS AND AGGREGATED DATA
output <- mbind(output,output_tot,output_EU,output_EU27,output_EUETS,output_EUETS_nonDE)
#totals concerning countries implementing min CO2 price is done above
#totals concerning countries KdW is done above
#INCLUDING ONLY CERTAIN VARIABLES
# settings mapping path
mappingvarsPath <- system.file("extdata","MappingVars.csv",package="limes")
# reading mapping file
mappingvars <- read.csv(mappingvarsPath,sep=";", dec = ".")
# write the *.mif or give back the magpie opject output
finalvars <- paste0(as.vector(mappingvars$LIMES)," (",as.vector(mappingvars$UnitLIMES) , ")")
output <- output[,,intersect(finalvars,getNames(output))]
#ERASING INTENSIVE VARIABLES (World, EU28, etc) for variables that were not weighted
# settings mapping path
mappingPath <- system.file("extdata","WeightedAverageVars.csv",package="limes")
# reading mapping file
mapping_int2ext <- read.csv(mappingPath,sep=";")
# Write the 2 sets: all intensive variables and those that were not averaged
#a) All
pos_intall <- match(mapping_int2ext$int, mappingvars$LIMES)
pos_intall <- pos_intall[!is.na(pos_intall)]
IntVars <- paste0(mappingvars$LIMES[pos_intall]," (",mappingvars$UnitLIMES[pos_intall],")")
#IntVars <- paste0(as.vector(mappingvars$LIMES[pos_intall])," (",as.vector(mappingvars$UnitLIMES[pos_intall]) , ")")
#Make sure this variable was indeed calculated
IntVars <- intersect(finalvars,IntVars)
#b) non-weighted intensive variables
mapping_int2ext_noweight <- mapping_int2ext[mapping_int2ext$ext == 0,]
pos_intnoweight <- match(mapping_int2ext_noweight$int, mappingvars$LIMES)
pos_intnoweight <- pos_intnoweight[!is.na(pos_intnoweight)]
IntVars_noweight <- paste0(mappingvars$LIMES[pos_intnoweight]," (",mappingvars$UnitLIMES[pos_intnoweight],")")
#Make sure this variable was indeed calculated
IntVars_noweight <- intersect(finalvars,IntVars_noweight)
IntVars_noweight <- setdiff(IntVars_noweight,c("Capacity|Electricity|Transmission Grid (GW)","Capacity|Electricity|Transmission Grid-km (GWkm)"))
if (length(intersect(getNames(output),IntVars)) > 0) {
output["GLO",,intersect(getNames(output),IntVars_noweight)] <- NA
output["EU28",,intersect(getNames(output),IntVars_noweight)] <- NA
output["EU27",,intersect(getNames(output),IntVars_noweight)] <- NA
output["EUETS",,intersect(getNames(output),IntVars_noweight)] <- NA
output["EUETS_nonDE",,intersect(getNames(output),IntVars)] <- NA
if (show_KdW == 1) {
output["KdW",,intersect(getNames(output),IntVars)] <- NA
output["nonKdW",,intersect(getNames(output),IntVars)] <- NA
output["KdW_EU",,intersect(getNames(output),IntVars)] <- NA
output["KdW_nonEU",,intersect(getNames(output),IntVars)] <- NA
output["nonKdW_EU",,intersect(getNames(output),IntVars)] <- NA
output["nonKdW_nonEU",,intersect(getNames(output),IntVars)] <- NA
}
if(length(minP) > 0) {
output["minP",,intersect(getNames(output),IntVars)] <- NA
output["non_minP",,intersect(getNames(output),IntVars)] <- NA
output["EU_non_minP",,intersect(getNames(output),IntVars)] <- NA
output["EUETS_non_minP",,intersect(getNames(output),IntVars)] <- NA
}
}
#Transmission capacity aggregated (special case)
if(length(intersect(getNames(output),"Capacity|Electricity|Transmission Grid (GW)")) > 0) {
output[c("GLO","EU28","EU27","EUETS"),,"Capacity|Electricity|Transmission Grid (GW)"] <-
output[c("GLO","EU28","EU27","EUETS"),,"Capacity|Electricity|Transmission Grid (GW)"]/2
output[c("GLO","EU28","EU27","EUETS"),,"Capacity|Electricity|Transmission Grid-km (GWkm)"] <-
output[c("GLO","EU28","EU27","EUETS"),,"Capacity|Electricity|Transmission Grid-km (GWkm)"]/2
}
#ADDING VARIABLES THAT ONLY EXIST FOR AN AGGREGATED REGION, e.g., the EU ETS cap
#(not possible to do as default for the region-dependent variables because mbind only works when dimensions do not differ)
# settings mapping path
AggVarPath <- system.file("extdata","AggregateVariables.csv",package="limes")
# reading mapping file
AggVarfile <- read.csv(AggVarPath,sep=";")
# write the *.mif or give back the magpie opject output
AggVars_tmp <- paste0(as.vector(AggVarfile$LIMES)," (",as.vector(AggVarfile$UnitLIMES) , ")")
AggVars <- intersect(AggVars_tmp,getNames(output))
#Depending on the configuration, some variables might have national values and thus should be extracted from the variables to be calculated for EU ETS
#and incorporated in the report as follows
#This variables appear already in output (from calculations or as fictitious variables), so we need to identify them (values differ from 0)
#Those which sum of values for region GLO (total) are 0, are those that have not been calculated
AggVars <- AggVars[which(dimSums(output["GLO",,AggVars], dim = 2) == 0)]
#Adding the corresponding values for the EU ETS
output_EUETSvars <- reportEUETSvars(gdx,output)[,time,] #dependent on reportEmissions
output["EUETS",,intersect(AggVars,getNames(output_EUETSvars))] <- output_EUETSvars[,,intersect(AggVars,getNames(output_EUETSvars))]
output_MSR <- reportMSR(gdx)[,time,]
output["EUETS",,intersect(AggVars,getNames(output_MSR))] <- output_MSR[,,intersect(AggVars,getNames(output_MSR))]
#Erase the values from the other fictitious variables for which there are no real values
output["EUETS",,setdiff(AggVars,union(getNames(output_EUETSvars),getNames(output_MSR)))] <- NA
#output["EUETS",,AggVars] <- reportEUETSvars(gdx)[,time,AggVars]
#Erasing the values for the remaining regions
output[setdiff(getItems(output, dim = 1),"EUETS"),,AggVars] <- NA
##UK ETS
#Adding the corresponding values for the EU ETS
output_UKETSvars <- reportUKETSvars(gdx,output)[,time,] #dependent on reportEUETSvars
output["GBR",,intersect(AggVars,getNames(output_UKETSvars))] <- output_UKETSvars[,,intersect(AggVars,getNames(output_UKETSvars))]
#INCLUDE HISTORICAL VALUES FOR THE INDUSTRY
#(source: "REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT AND THE COUNCIL Report on the functioning of the European carbon market COM/2019/557 final/2" https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52019DC0557R(01))
if(!is.na(match("Emissions|CO2|Industry (Mt CO2/yr)",getNames(output)))) {
output["EUETS",2010,"Emissions|CO2|Industry (Mt CO2/yr)"] <- NA #715 #Value from 2011 (2010 not available) - emissions reported from data viewer (537.4) might be incomplete as some could be included in combustion
#output["EUETS",2015,"Emissions|CO2|Industry (Mt CO2/yr)"] <- 771 #emissions reported from data viewer (590.8) might be incomplete as some could be included in combustion
output["EUETS",2010,"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"] <- NA #output["EUETS",2010,"Emissions|CO2|Industry (Mt CO2/yr)"] + output["EUETS",2010,"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"]
#output["EUETS",2015,"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"] <- output["EUETS",2015,"Emissions|CO2|Industry (Mt CO2/yr)"] + output["EUETS",2015,"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"]
if(!is.na(match("Emissions|CO2|EU ETS (Mt CO2/yr)",getNames(output)))) {
output["EUETS",2010,"Emissions|CO2|EU ETS (Mt CO2/yr)"] <- NA #output["EUETS",2010,"Emissions|CO2|Industry (Mt CO2/yr)"] + output["EUETS",2010,"Emissions|CO2|EU ETS (Mt CO2/yr)"]
#output["EUETS",2015,"Emissions|CO2|EU ETS (Mt CO2/yr)"] <- output["EUETS",2015,"Emissions|CO2|Industry (Mt CO2/yr)"] + output["EUETS",2015,"Emissions|CO2|EU ETS (Mt CO2/yr)"]
}
#To avoid confusion, make sure that industry-related values are not reported for the EU28
output["EU28",c(2010,2015),"Emissions|CO2|Industry (Mt CO2/yr)"] <- NA
output["EU27",c(2010,2015),"Emissions|CO2|Industry (Mt CO2/yr)"] <- NA
output["EU28",c(2010,2015),"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"] <- NA
output["EU27",c(2010,2015),"Emissions|CO2|Electricity and Industry (Mt CO2/yr)"] <- NA
#Add NA for 2010. Because the MAC industry only applies from 2015, the reported price is the sum
if(is.na(output[c("GLO"),c(2010),"Emissions|CO2|Industry (Mt CO2/yr)"])) {
output[c("EU28","EUETS","EU27","GLO"),c(2010),"Price|Carbon|Net|Industry (Eur2010/t CO2)"] <- NA
output[c("EU28","EUETS","EU27","GLO"),c(2010),"Price|Carbon|National Climate Target|Industry (Eur2010/t CO2)"] <- NA
}
}
#SCALING THE RESULTS ACCORDING TO THE UNITS SPECIFIES FOR THE PROJECT
tmp <- NULL
for (i in seq_len(length(getNames(output)))) {
tmp <- mbind(tmp,output[,,i]*mappingvars[match(getNames(output)[i],finalvars),]$ConvFactor)
}
output_f <- tmp
#output<-tmp
#output_f<-output
#MAPPING THE VARIABLES TO THOSE OF A SPECIFIC PROJECT
posvarsmapping <- match(getNames(output_f),finalvars)
posvarsmapping <- posvarsmapping[which(posvarsmapping != "NA")]
mappingvars_project <- paste0(as.vector(mappingvars[posvarsmapping,]$Enavi)," (",as.vector(mappingvars[posvarsmapping,]$UnitEnavi) , ")")
output_f <- setNames(output_f,mappingvars_project)
#REPORT FIGURES
#reportFigures(gdx,output_f)
#WRITE REPORT
#load the model version
c_LIMESversion <- readGDX(gdx,name="c_LIMESversion",field="l",format="first_found") #model version
if(!is.null(file)) {
write.report(output_f,model=paste0("LIMES_EU_v",c_LIMESversion),scenario=scenario,file=file,ndigit=7)
}
#if(!is.null(file)) write.reportProject(paste0("LIMES_generic_",scenario,".mif"),mappingvars,model="LIMES_EU",scenario=scenario,file=file,ndigit=7)
invisible(output_f)
#################################################################
#ONLY FOR THE REPORT OF INPUTS (run manually)
#################################################################
#Always keep this in 0!!!!
#c_reportinput <- 0
#
#if(c_reportinput != 0) {
# #time=as.numeric(readGDX(gdx,name="t"))
#
# #Files-related variables
# #outputdir <- "C:/Users/osorio/Nextcloud/PIK/Data for LIMES/CSV files/output" # path to the output folder
# limes_reporting_file <- file.path(outputdir,paste0("LIMES_inputparam.mif"))
# file=limes_reporting_file
# #lastDir <- splitPath[[1]][length(splitPath[[1]])]
# #scenario <- lastDir
#
# #initialize report variable
# output <- NULL
#
# #adding fuel costs to report output
# output <- mbind(output,reportFuelCosts(gdx)[,time,])
#
# #Adding all the input parameters (except for fuel costs) -> turn on switch (c_reportheating) if heat-related input is to be reported
# output <- mbind(output,reportInput(gdx)[,time,])
#
# #Clean the output
# output_glo <- NULL
# var_dup <- NULL
# n_regi <- length(unique(getItems(output, dim = 1)))
# n_years <- length(getYears(output))
# for (var_name in getNames(output)) {
# #Create array to save whether the number is duplicated for all REGI in one YEAR
# dup_year <- new.magpie(cells_and_regions = "GLO", years = getYears(output), names = NULL,
# fill = 0, sort = FALSE, sets = NULL)
# #Create array to save duplicated values in just one ("GLO") array
# #output_tmp <- new.magpie(cells_and_regions = "GLO", years = getYears(output), names = NULL,
# # fill = 0, sort = FALSE, sets = NULL)
#
# for (tt in getYears(output)) {
# #Check if the value is duplicated for all REGI in one YEAR
# if(sum(duplicated(output[,tt,var_name])) == n_regi-1) {
# dup_year[,tt,] <- 1
# }
# }
#
# #Check if duplicates occur over all YEARS
# #output_tmp <- NULL
# if(dimSums(dup_year[,,],dim=2)==n_years) {
# #Allocate DEU values to the unique "GLO" variable
# output_tmp <- output["DEU",,var_name]
# var_dup <- c(var_dup,var_name)
# } else {
# output_tmp <- 0*output["DEU",,var_name]
# }
# getItems(output_tmp, dim = 1) <- "GLO"
#
# #Concatenate all "GLO" variables
# output_glo <- mbind(output_glo,output_tmp)
# }
#
# #Concatenate "GLO" variables with (surviving) region-dependent parameters
# output_f <- mbind(output_glo,output)
#
# #Clean the file
# #Erase region-dependent data for variables with duplicates
# output_f[setdiff(getItems(output_f, dim = 1),"GLO"),,var_dup] <- NA
# #Erase "GLO" data for variables without duplicates
# output_f["GLO",,setdiff(getNames(output_f),var_dup)] <- NA
#
# c_LIMESversion <- readGDX(gdx,name="c_LIMESversion",field="l",format="first_found") #model version
# if(!is.null(file)) write.report(output_f,model=paste0("LIMES_EU_v",c_LIMESversion),scenario=scenario,file=file,ndigit=7)
#}
}
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